arXiv Statistics ML
Recertifying decisions via e-closure: uniform improvements of conformal risk control and randomized e-BH
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
Sep 30
ReCIRC: Rectified Conformal Risk Control
arXiv:2609.38112v1 Announce Type: cross Abstract: Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or misse...
arXiv Machine Learning
Jun 30
When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
arXiv:2606. 29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.
arXiv Machine Learning
Jul 3
Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
arXiv Machine Learning
Aug 14
Chance-constrained selection of sequential intervention strategies from counterfactual estimates
arXiv:2608. 13209v1 Announce Type: cross Abstract: Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget.
arXiv Machine Learning
Aug 18
CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules
arXiv:2608. 14761v1 Announce Type: cross Abstract: At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evaluate before each regret update.
arXiv Machine Learning
Jul 10
Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).